Dynamic sensor range in advanced driver assistance systems

ABSTRACT

Various systems and methods for implementing dynamic sensor range in advanced driver assistance systems are described herein. A system for managing an autonomous vehicle comprises a vehicle control system in a vehicle to detect a speed of the vehicle and adjust a forward-facing camera array based on the speed of the vehicle.

TECHNICAL FIELD

Embodiments described herein generally relate to vehicle controls and inparticular, to using dynamic sensor range in advanced driver assistancesystems.

BACKGROUND

In the automotive context, advanced driver assistance systems (ADAS)systems are those developed to automate, adapt, or enhance vehiclesystems to increase safety and provide better driving. In such systems,safety features are designed to avoid collisions and accidents byoffering technologies that alert the driver to potential problems, or toavoid collisions by implementing safeguards and taking over control ofthe vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. Some embodiments are illustrated by way of example, and notlimitation, in the figures of the accompanying drawings in which:

FIG. 1 is a schematic drawing illustrating a system to control avehicle, according to an embodiment;

FIG. 2 is a schematic diagram of an image processing configuration,according to an embodiment;

FIG. 3 is a data and control flow diagram illustrating a process tomanage a camera array based on vehicle speed, according to anembodiment;

FIG. 4 is a flowchart illustrating a method of augmenting vehiclesensors, according to an embodiment; and

FIG. 5 is a block diagram illustrating an example machine upon which anyone or more of the techniques (e.g., methodologies) discussed herein mayperform, according to an example embodiment.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of some example embodiments. It will be evident, however,to one skilled in the art that the present disclosure may be practicedwithout these specific details.

Systems and methods described herein implement dynamic sensor ranges inadvanced driver assistance systems (ADAS). ADAS includes variousforward, sideward, and rearward facing sensors in a vehicle. The sensorsinclude radar, LIDAR, cameras, ultrasound, infrared, and other sensorsystems. Front-facing sensors may be used for adaptive cruise control,parking assistance, lane departure, collision avoidance, pedestriandetection, and the like. Many of these types of systems implementdifficult image analysis or other types of analysis that requiresintense computing power. Conventional systems are able to process dataout to a distance of about 200 meters ahead of the vehicle. As thevehicle increases forward speed, the amount of time to process such datais reduced until at some point the systems are unable to process thedata before the vehicle needs to react to it. In effect, the vehicle isoutrunning the processing range of the sensor systems. What is needed isa mechanism to augment the processing efficiency of ADAS and relatedsystems to provide sufficient reaction time for the driver and thevehicle.

FIG. 1 is a schematic drawing illustrating a system 100 to control avehicle, according to an embodiment. FIG. 1 includes a vehicle controlsystem 102, a vehicle 104, and a cloud service 106, communicativelycoupled via a network 108.

The vehicle 104 may be of any type of vehicle, such as a commercialvehicle, a consumer vehicle, a recreation vehicle, a car, a truck, amotorcycle, or a boat, able to operate at least partially in anautonomous mode. The vehicle 104 may operate at some times in a manualmode where the driver operates the vehicle 104 conventionally usingpedals, steering wheel, and other controls. At other times, the vehicle104 may operate in a fully autonomous mode, where the vehicle 104operates without user intervention. In addition, the vehicle 104 mayoperate in a semi-autonomous mode, where the vehicle 104 controls manyof the aspects of driving, but the driver may intervene or influence theoperation using conventional (e.g., steering wheel) and non-conventionalinputs (e.g., voice control).

The vehicle 104 includes a sensor array, which may include variousforward, side, and rearward facing cameras, radar, LIDAR, ultrasonic, orsimilar sensors. Forward-facing is used in this document to refer to theprimary direction of travel, the direction the seats are arranged toface, the direction of travel when the transmission is set to drive, orthe like. Conventionally then, rear-facing or rearward-facing is used todescribe sensors that are directed in a roughly opposite direction thanthose that are forward or front-facing. It is understood that somefront-facing camera may have a relatively wide field of view, even up to180-degrees. Similarly, a rear-facing camera that is directed at anangle (perhaps 60-degrees off center) to be used to detect traffic inadjacent traffic lanes, may also have a relatively wide field of view,which may overlap the field of view of the front-facing camera.Side-facing sensors are those that are directed outward from the sidesof the vehicle. Cameras in the sensor array may include infrared orvisible light cameras, able to focus at long-range or short-range withnarrow or large fields of view.

The vehicle 104 includes an on-board diagnostics system to recordvehicle operation and other aspects of the vehicle's performance,maintenance, or status. The vehicle 104 may also include various othersensors, such as driver identification sensors (e.g., a seat sensor, aneye tracking and identification sensor, a fingerprint scanner, a voicerecognition module, or the like), occupant sensors, or variousenvironmental sensors to detect wind velocity, outdoor temperature,barometer pressure, rain/moisture, or the like.

The cloud service 106 may be provided as software as a service, acentral computing platform, a virtualized computing platform, or thelike. The could service 106 may collect data from the vehicle 104, thevehicle control system 102, or occupants of the vehicle 104, to provideservices to the vehicle 104, the occupants of the vehicle 104, or otherpeople or vehicles. In an aspect, the cloud service 106 collects datafrom one or more vehicles or occupants of one or more vehicles, andcreates a model of a terrain, road, route, bridge, or other travelstructure. The model may then be used by other vehicles or occupants ofvehicles to adjust sensor arrays on the vehicles. As an example, thecloud service 106 may receive data about a certain lane change in aroad, where the road narrows and the lane change is difficult tonavigate in the presence of traffic. The model may be used by vehiclesthat travel over the road to increase the sensitivity, resolution,processing power, or other aspect of the sensor array to better analyzethe road and successfully navigate the lane change. The vehicles thatlater travel on the road may also upload data regarding their traversalof the road, which may be used to refine the model.

In addition, the cloud service 106 may be used as a photo or videorepository by a driver of the vehicle 104. In some examples, the cameraarray in the vehicle 104 may be used to capture images or video and suchimages/video may be stored in a location at the cloud service 106 forlater retrieval. As an example, the driver may view a scenic pond whiledriving. Using a gesture, voice command, gaze detection, or other input,the camera array may be controlled to capture an image or video of thescene and upload it to the cloud service 106. In this manner, the driveris able to capture images/videos of the surroundings with lessdistraction than if using a conventional hand-held camera.

The network 108 may include local-area networks (LAN), wide-areanetworks (WAN), wireless networks (e.g., 802.11 or cellular network),the Public Switched Telephone Network (PSTN) network, ad hoc networks,personal area networks (e.g., Bluetooth), vehicle-based networks (e.g.,Controller Area Network (CAN) BUS), or other combinations orpermutations of network protocols and network types. The network 108 mayinclude a single local area network (LAN) or wide-area network (WAN), orcombinations of LANs or WANs, such as the Internet. The various devices(e.g., mobile device 106 or vehicle 104) coupled to the network 108 maybe coupled to the network 108 via one or more wired or wirelessconnections.

In operation, the vehicle 104 adaptively implements one or more sensors,or alters the operation of one or more sensors, in response to the stateor location of the vehicle 104. In an example, the vehicle controlsystem 102 is configured to detect the speed of the vehicle 104 and whenthe speed is faster than a threshold speed, the vehicle control system102 modifies one or more cameras in a camera array on the vehicle 104.The modification may be to implement additional cameras in the cameraarray, modify the image processing of images captured by one or morecameras of the camera array, modify the focal length, zoom, or otheroperational aspects of one or more cameras of the camera array, or othersuch adjustments to provide time for sensor processing.

In another example, the vehicle control system 102 is configured tochange the resolution, focal length, or zoom of one or more cameras inthe camera array of the vehicle 104 based on speed. Similar to howhigh-beam headlights work, adjustments to cameras may be used to lookfarther ahead, narrow the field of view to focus on objects in thedistance, or alter the resolution of the image to recognize objectfarther away. In an aspect, the vehicle control system 102 controls oneor more cameras to zoom out farther from the vehicle 104 as thevehicle's speed increases. In another aspect, the vehicle control system102 may control one or more cameras to increase the image resolution sothat objects that are farther away have enough pixel density (e.g.,dots-per-inch (DPI)) to classify and recognize objects with specificity.Low-resolution images (e.g., those with low DPI) may have large blockypixels that do not provide enough distinct shapes to recognize letters,sign shapes, or pictographs, for example. Increasing the resolution ofthe image comes at a cost though—increased processing time to analyzethe image. However, by reducing the amount of the image to process, suchas by artificially implementing a limited field of view, the vehiclecontrol system 102 is able to process images of objects farther away andignore or filter objects that are nearer.

In another example the vehicle control system 102 implements multiplecameras for differing purposes. One forward-facing camera (or severalcameras) is used to capture a low-resolution image. A simple classifiermay be used on the low-resolution image to identify potential objects ofinterest. The locations of these potential objects of interest are thenrelayed to an image processor. The image processor may obtain ahigh-resolution image from another forward-facing camera or cameras. Thehigh-resolution image may be of the object of interest (e.g., focusedand zoomed toward a particular object as identified by the simpleclassifier) or may be of substantially the same scene (e.g., similarfield of view) of the low-resolution image and cropped to isolate theobject of interest. The image processor may obtain a high-resolutionimage of the potential object(s) of interest and use the high-resolutionimage in a complex classifier to determine additional information aboutthe object. The two-tier layered approach improves sensor efficiency toaccommodate faster moving vehicles.

In an embodiment, the vehicle control system 102 includes a camera arrayinterface 110, an image processor 112, a sensor fusion module 114, and acommunication module 118. The vehicle control system 102 operates as asystem to manage sensors in and around the vehicle 104. The camera arrayinterface 110 is operable to directly or indirectly control one or morecameras. The camera(s) may be activated or deactivated; directed tofocus on an object or an area; controlled to zoom in or out of a scene;controlled to capture images or video for the user's later reference; orthe like.

The image processor 112 is operable to implement one or more objectrecognition algorithms or classifiers. Various methods may be usedincluding, but not limited to edge matching, divide-and-conquersearching, greyscale matching, gradient matching, histogram analysis,and machine learning (e.g., genetic algorithms). The image processor 112may implement relatively simple classifiers to identify potentialobjects of interest in a low-resolution image. The image processor 112may also implement relatively complex classifiers to more specificallyidentify an object of interest in a high-resolution image. Workingtogether, the simple and complex classifiers provide a cascadingworkflow that improves processing abilities of the image processor 112.Some or all of the processing performed by the image processor 112 maybe offloaded to a remote system (e.g., the cloud service). Whileoffloading image processing to a large cloud service may decreaseprocessing time, the communication overhead may make the entire processtake longer. As such, depending on the communication abilities of thevehicle 104, offloading may not be used. For example, if the vehicle 104is travelling in mountainous regions where a cellular signal is weak,the image processing may be performed at the vehicle 104 because thecommunication overhead is too great.

The sensor fusion module 114 may be used to fuse multiple inputs andmanage multiple sensors. The inputs may be from the sensors in thevehicle 104 or from external sources, such as the cloud service 106. Inan example, the sensor fusion module 114 obtains a model of a portion ofa road and uses the model to modify operational characteristics of oneor more sensors on the vehicle 104 in order to provide safer operation.

The communication module 116 is operable to communicate with at leastthe cloud service 106. The communication module 116 may providecommunication for other components of the vehicle control system 102,such as the image processor 112 or the sensor fusion module 114. Thecommunication module 116 may use one or multiple communicationmodalities including, but not limited to wireless networks (e.g., 802.11or cellular network), ad hoc networks, personal area networks (e.g.,Bluetooth), vehicle-based networks (e.g., CAN BUS), or othercombinations or permutations of network protocols and network types.

The vehicle control system 102 may be disposed in the vehicle 104 or ina network server (e.g., cloud service 106). The vehicle control system102 may be installed as an after-market component of the vehicle, or maybe provided as a manufacturer option. Portions of the vehicle controlsystem 102 may be implemented in several places, such as in adistributed computing model. For example, the imaging processing forobject recognition may be provided by the cloud service 106 or anothercomputing platform. As another example, the communication module 116 maybe provided at least in part in a user device, such as a smartphone. Insuch an example, the vehicle 104 may communicate with the user deviceover a short-range telemetry (e.g., Bluetooth) and the smartphone maythen communicate with the cloud service 106 over long-range telemetry(e.g., cellular).

Thus, in various embodiments, a system for managing a vehicle isillustrated in FIG. 1, the system comprising a vehicle control system102 in a vehicle 104 to detect a speed of the vehicle 104 and adjust aforward-facing camera array based on the speed of the vehicle 104.

In an embodiment, to adjust the camera array, the vehicle control system102 is to determine whether the speed of the vehicle violates athreshold speed, control a low-resolution camera in the camera array tocapture a low-resolution image, and control a high-resolution camera inthe camera array to capture an object identified in the low-resolutionimage. The threshold speed may be configurable by the driver or owner ofthe vehicle 104. Alternatively, the threshold speed may be configured bythe manufacturer or provider of the camera array or the vehicle 104. Inan embodiment, the threshold speed is a contact value, such as 60 milesper hour, and is based on how much time it takes to process images andrecognize objects. In another embodiment, the threshold speed is avariable value and may be based on the actual performance (e.g., amoving 10 minute window) of the image processing. The variable thresholdspeed has an advantage of adjusting for changing conditions outside thevehicle 104, such as when there is snow, rain, fog, or otherenvironmental conditions that may increase the processing time neededfor object classification.

In an embodiment, to control the high-resolution camera comprise, thevehicle control system 102 is to identify an object of interest in thelow-resolution image using a simple object classifier, determine alocation of the object of interest in the low-resolution image,determine a portion of a high-resolution image captured by thehigh-resolution camera that corresponds to the location of the object ofinterest in the low-resolution image, and process the portion of thehigh-resolution image using a complex object classifier. Such imageprocessing may be performed by a subsystem of the vehicle control system102, such as the image processor 112.

In another embodiment, to control the high-resolution camera comprise,the vehicle control system 102 is to identify an object of interest inthe low-resolution image using a simple object classifier, determine alocation of the object of interest in the low-resolution image, controlthe high-resolution camera to frame the object of interest and capture ahigh-resolution image, and process the high-resolution image using acomplex object classifier. In such an embodiment, the aim, focus, andzoom controls of the high-resolution camera may be based on a trajectorycalculated using a relative angular offset of the high-resolution camerafrom the low-resolution camera, in addition to the relative location ofthe object of interest in the field of view of the low-resolutioncamera. Thus, in a further embodiment, to control the high-resolutioncamera to frame the object of interest comprise, the vehicle controlsystem 102 is to control at least one of a zoom or a focus to frame theobject of interest.

In an embodiment, to adjust the camera array comprise, the vehiclecontrol system 102 is to adjust one of a focal length or a resolution toidentify objects farther away from the vehicle when the speed of thevehicle increases. For example, by using a high-resolution image,details of objects that are farther away may be distinguishable.

In an embodiment, to adjust the camera array comprise, the vehiclecontrol system 102 is to focus a camera in the camera array on objectsfarther in front of the vehicle as the speed of the vehicle increases.The camera in the camera array may consequently also have a smallerfield of view. In this case, the camera may obtain higher resolutionimages in a smaller frame so as to not impact the overall imageprocessing time needed to detect objects of interest in the frame.

In an embodiment, the system of FIG. 1 includes a sensor fusion module114 to access a model of a road in a route that the vehicle istravelling, the road having a feature indicating a dangerous portion ofthe road, use the camera array to recognize the feature, and adjust asensor in the vehicle when the feature is recognized. Dangerous portionsof the road may be narrow lanes, dangerous objects near the road, blindintersections, or the like. When a dangerous portion of the road isahead, the sensors may be configured to be more sensitive to provideadditional safety to the occupants of the vehicle 104. In an embodiment,to adjust the sensor, the sensor fusion module 114 is to increase aresolution of a camera in the camera array. In another embodiment, toadjust the sensor comprise, the sensor fusion module 114 is to increasea sampling rate of the sensor. In a further embodiment, the sensor maybe one of: a camera, a radar sensor, a LIDAR sensor, an ultrasonicsensor, or an infrared sensor.

In an embodiment, to access the model in the route, the sensor fusionmodule 114 is to determine a location or a route of the vehicle,transmit the location or the route of the vehicle to a cloud service(e.g., cloud service 106), and receive from the cloud service, the modelof the road in the route. The location or route of the vehicle 104 maybe obtained from an on-board navigation unit in the vehicle 104, whichmay have a pre-planned route, a current location, a destination, orother information about the vehicle's location and surroundings. Thelocation may be obtained from a geographic location system, such as aglobal positioning system (GPS) or Global Navigation Satellite System(GLONASS).

In an embodiment, the model is based on a plurality of traversals overthe road made by previous vehicles. For example, other vehicles equippedwith similar vehicle control systems may upload features, objects, orother information from images obtained by their on-board camera arrays.Using the collective data, the cloud service 106 may generate a modelfor a road or a portion of a road. In an embodiment, the model isrevised using a machine learning technique. Based on user feedback, forexample, a back propagating machine learning technique may be used torefine the image classifiers.

In an embodiment, the system of FIG. 1 includes an image capture moduleto determine a gaze direction of a driver of the vehicle and process animage from a camera of the camera array based on the gaze direction. Ina further embodiment, to process the image from the camera of the cameraarray based on the gaze direction, the image capture module is tocapture the image and transmit the image to a cloud service. The imagecapture module may interface with the communication module 116 totransmit the image.

In an embodiment, to process the image from the camera of the cameraarray based on the gaze direction, the image capture module is to adjustthe camera array based on the image to identify an object of interest inthe image and advise the driver based on the identification of theobject of interest. For example, if the driver is looking at an objectthat may be a pedestrian, the image capture module may adjust the cameraarray to capture various representations of the object (e.g., aninfrared image and a visible light image) in order to determine a likelyclassification of the object. If there is a sufficient confidence in aclassification, the image capture module may advise the driver that itis likely a person. The advice may be provided using various methods,such as a heads up display on the windshield of the vehicle 104, anaudible notification, a pictographic representation on the dashboard,etc.

FIG. 2 is a schematic diagram of an image processing configuration,according to an embodiment. FIG. 2 includes a short-range camera 200, along-range camera 202, a vehicle controller 204, and an image processor206. Based on vehicle speed detected by the vehicle controller 204 anddriver's gaze direction and focus, the short-range camera 200 may beaimed in the direction of the scene and take a high-definition photo.The photo may then be uploaded to the cloud for the user to later accessthem, for example, to copy them to personal folders or discard them. Thearrangement of cameras 200, 202 and the vehicle controller 204 asdepicted in FIG. 2 may also be used for the tiered image processingdiscussed above.

FIG. 3 is a data and control flow diagram illustrating a process tomanage a camera array based on vehicle speed, according to anembodiment. At 300, vehicle input is received (e.g., vehicle speed). Thevehicle input may be received from a speedometer sensor connected to theCAN BUS. In another example, the vehicle input may be received from apositioning system, such as a GPS receiver. If the vehicle speed is lessthan a threshold (e.g., 65 miles per hour), then nearby potentialobjects are detected (operation 302), and objects are resolved from thepotential objects detected (operation 304). If the vehicle speed exceedsthe threshold, then far potential objects are detected (operation 306),which are then resolved in the operate 304.

FIG. 4 is a flowchart illustrating a method 400 of augmenting vehiclesensors, according to an embodiment. At block 402, at a vehicle controlsystem in a vehicle, a speed of the vehicle is detected.

At block 404, a forward-facing camera array based on the speed of thevehicle is adjusted by the vehicle control system.

In an embodiment, adjusting the camera array comprises determiningwhether the speed of the vehicle violates a threshold speed, controllinga low-resolution camera in the camera array to capture a low-resolutionimage, and controlling a high-resolution camera in the camera array tocapture an object identified in the low-resolution image. In a furtherembodiment, controlling the high-resolution camera comprises identifyingan object of interest in the low-resolution image using a simple objectclassifier, determining a location of the object of interest in thelow-resolution image, determining a portion of a high-resolution imagecaptured by the high-resolution camera that corresponds to the locationof the object of interest in the low-resolution image, and processingthe portion of the high-resolution image using a complex objectclassifier. In another embodiment, controlling the high-resolutioncamera comprises identifying an object of interest in the low-resolutionimage using a simple object classifier, determining a location of theobject of interest in the low-resolution image, controlling thehigh-resolution camera to frame the object of interest and capture ahigh-resolution image, and processing the high-resolution image using acomplex object classifier. In a further embodiment, controlling thehigh-resolution camera to frame the object of interest comprisescontrolling at least one of a zoom or a focus to frame the object ofinterest.

In an embodiment, adjusting the camera array comprises adjusting one ofa focal length or a resolution to identify objects farther away from thevehicle when the speed of the vehicle increases.

In an embodiment, adjusting the camera array comprises focusing a camerain the camera array on objects farther in front of the vehicle as thespeed of the vehicle increases.

In an embodiment, the method 400 includes accessing a model of a road ina route that the vehicle is travelling, the road having a featureindicating a dangerous portion of the road, using the camera array torecognize the feature, and adjusting a sensor in the vehicle when thefeature is recognized. In a further embodiment, adjusting the sensorcomprises increasing a resolution of a camera in the camera array. Inanother embodiment, wherein adjusting the sensor comprises increasing asampling rate of the sensor. In embodiments, the sensor comprises oneof: a camera, a radar sensor, a LIDAR sensor, an ultrasonic sensor, oran infrared sensor.

In an embodiment, accessing the model in the route comprises determininga location or a route of the vehicle, transmitting the location or theroute of the vehicle to a cloud service, and receiving from the cloudservice, the model of the road in the route.

In an embodiment, the model is based on a plurality of traversals overthe road made by previous vehicles.

In an embodiment, the model is revised using a machine learningtechnique.

In an embodiment, the method 400 includes determining a gaze directionof a driver of the vehicle and processing an image from a camera of thecamera array based on the gaze direction. In a further embodiment,processing the image from the camera of the camera array based on thegaze direction comprises capturing the image and transmitting the imageto a cloud service.

In another embodiment, processing the image from the camera of thecamera array based on the gaze direction comprises adjusting the cameraarray based on the image to identify an object of interest in the imageand advising the driver based on the identification of the object ofinterest.

Embodiments may be implemented in one or a combination of hardware,firmware, and software. Embodiments may also be implemented asinstructions stored on a machine-readable storage device, which may beread and executed by at least one processor to perform the operationsdescribed herein. A machine-readable storage device may include anynon-transitory mechanism for storing information in a form readable by amachine (e.g., a computer). For example, a machine-readable storagedevice may include read-only memory (ROM), random-access memory (RAM),magnetic disk storage media, optical storage media, flash-memorydevices, and other storage devices and media.

A processor subsystem may be used to execute the instruction on themachine-readable medium. The processor subsystem may include one or moreprocessors, each with one or more cores. Additionally, the processorsubsystem may be disposed on one or more physical devices. The processorsubsystem may include one or more specialized processors, such as agraphics processing unit (GPU), a digital signal processor (DSP), afield programmable gate array (FPGA), or a fixed function processor.

Examples, as described herein, may include, or may operate on, logic ora number of components, modules, or mechanisms. Modules may be hardware,software, or firmware communicatively coupled to one or more processorsin order to carry out the operations described herein. Modules may behardware modules, and as such modules may be considered tangibleentities capable of performing specified operations and may beconfigured or arranged in a certain manner. In an example, circuits maybe arranged (e.g., internally or with respect to external entities suchas other circuits) in a specified manner as a module. In an example, thewhole or part of one or more computer systems (e.g., a standalone,client or server computer system) or one or more hardware processors maybe configured by firmware or software (e.g., instructions, anapplication portion, or an application) as a module that operates toperform specified operations. In an example, the software may reside ona machine-readable medium. In an example, the software, when executed bythe underlying hardware of the module, causes the hardware to performthe specified operations. Accordingly, the term hardware module isunderstood to encompass a tangible entity, be that an entity that isphysically constructed, specifically configured (e.g., hardwired), ortemporarily (e.g., transitorily) configured (e.g., programmed) tooperate in a specified manner or to perform part or all of any operationdescribed herein. Considering examples in which modules are temporarilyconfigured, each of the modules need not be instantiated at any onemoment in time. For example, where the modules comprise ageneral-purpose hardware processor configured using software; thegeneral-purpose hardware processor may be configured as respectivedifferent modules at different times. Software may accordingly configurea hardware processor, for example, to constitute a particular module atone instance of time and to constitute a different module at a differentinstance of time. Modules may also be software or firmware modules,which operate to perform the methodologies described herein.

FIG. 5 is a block diagram illustrating a machine in the example form ofa computer system 500, within which a set or sequence of instructionsmay be executed to cause the machine to perform any one of themethodologies discussed herein, according to an example embodiment. Inalternative embodiments, the machine operates as a standalone device ormay be connected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of either a serveror a client machine in server-client network environments, or it may actas a peer machine in peer-to-peer (or distributed) network environments.The machine may be an onboard vehicle system, wearable device, personalcomputer (PC), a tablet PC, a hybrid tablet, a personal digitalassistant (PDA), a mobile telephone, or any machine capable of executinginstructions (sequential or otherwise) that specify actions to be takenby that machine. Further, while only a single machine is illustrated,the term “machine” shall also be taken to include any collection ofmachines that individually or jointly execute a set (or multiple sets)of instructions to perform any one or more of the methodologiesdiscussed herein. Similarly, the term “processor-based system” shall betaken to include any set of one or more machines that are controlled byor operated by a processor (e.g., a computer) to individually or jointlyexecute instructions to perform any one or more of the methodologiesdiscussed herein.

Example computer system 500 includes at least one processor 502 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU) or both,processor cores, compute nodes, etc.), a main memory 504 and a staticmemory 506, which communicate with each other via a link 508 (e.g.,bus). The computer system 500 may further include a video display unit510, an alphanumeric input device 512 (e.g., a keyboard), and a userinterface (UI) navigation device 514 (e.g., a mouse). In one embodiment,the video display unit 510, input device 512 and UI navigation device514 are incorporated into a touch screen display. The computer system500 may additionally include a storage device 516 (e.g., a drive unit),a signal generation device 518 (e.g., a speaker), a network interfacedevice 520, and one or more sensors (not shown), such as a globalpositioning system (GPS) sensor, compass, accelerometer, or othersensor.

The storage device 516 includes a machine-readable medium 522 on whichis stored one or more sets of data structures and instructions 524(e.g., software) embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 524 mayalso reside, completely or at least partially, within the main memory504, static memory 506, and/or within the processor 502 during executionthereof by the computer system 500, with the main memory 504, staticmemory 506, and the processor 502 also constituting machine-readablemedia.

While the machine-readable medium 522 is illustrated in an exampleembodiment to be a single medium, the term “machine-readable medium” mayinclude a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe one or more instructions 524. The term “machine-readable medium”shall also be taken to include any tangible medium that is capable ofstoring, encoding or carrying instructions for execution by the machineand that cause the machine to perform any one or more of themethodologies of the present disclosure or that is capable of storing,encoding or carrying data structures utilized by or associated with suchinstructions. The term “machine-readable medium” shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media. Specific examples of machine-readable mediainclude non-volatile memory, including but not limited to, by way ofexample, semiconductor memory devices (e.g., electrically programmableread-only memory (EPROM), electrically erasable programmable read-onlymemory (EEPROM)) and flash memory devices; magnetic disks such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks.

The instructions 524 may further be transmitted or received over acommunications network 526 using a transmission medium via the networkinterface device 520 utilizing any one of a number of well-knowntransfer protocols (e.g., HTTP). Examples of communication networksinclude a local area network (LAN), a wide area network (WAN), theInternet, mobile telephone networks, plain old telephone (POTS)networks, and wireless data networks (e.g., Wi-Fi, 3G, and 4G LTE/LTE-Aor WiMAX networks). The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding, orcarrying instructions for execution by the machine, and includes digitalor analog communications signals or other intangible medium tofacilitate communication of such software.

ADDITIONAL NOTES & EXAMPLES

Example 1 is a system for managing an autonomous vehicle, the systemcomprising: a vehicle control system in a vehicle to: detect a speed ofthe vehicle; and adjust a forward-facing camera array based on the speedof the vehicle.

In Example 2, the subject matter of Example 1 optionally includes,wherein to adjust the camera array, the vehicle control system is to:determine whether the speed of the vehicle violates a threshold speed;control a low-resolution camera in the camera array to capture alow-resolution image; and control a high-resolution camera in the cameraarray to capture an object identified in the low-resolution image.

In Example 3, the subject matter of Example 2 optionally includes,wherein to control the high-resolution camera, the vehicle controlsystem is to: identify an object of interest in the low-resolution imageusing a simple object classifier; determine a location of the object ofinterest in the low-resolution image; determine a portion of ahigh-resolution image captured by the high-resolution camera thatcorresponds to the location of the object of interest in thelow-resolution image; and process the portion of the high-resolutionimage using a complex object classifier.

In Example 4, the subject matter of any one or more of Examples 2-3optionally include, wherein to control the high-resolution camera, thevehicle control system is to: identify an object of interest in thelow-resolution image using a simple object classifier; determine alocation of the object of interest in the low-resolution image; controlthe high-resolution camera to frame the object of interest and capture ahigh-resolution image; and process the high-resolution image using acomplex object classifier.

In Example 5, the subject matter of Example 4 optionally includes,wherein to control the high-resolution camera to frame the object ofinterest, the vehicle control system is to: control at least one of azoom or a focus to frame the object of interest.

In Example 6, the subject matter of any one or more of Examples 1-5optionally include, wherein to adjust the camera array, the vehiclecontrol system is to: adjust one of a focal length or a resolution toidentify objects farther away from the vehicle when the speed of thevehicle increases.

In Example 7, the subject matter of any one or more of Examples 1-6optionally include, wherein to adjust the camera array, the vehiclecontrol system is to: focus a camera in the camera array on objectsfarther in front of the vehicle as the speed of the vehicle increases.

In Example 8, the subject matter of any one or more of Examples 1-7optionally include, further comprising a sensor fusion module to: accessa model of a road in a route that the vehicle is travelling, the roadhaving a feature indicating a dangerous portion of the road; use thecamera array to recognize the feature; and adjust a sensor in thevehicle when the feature is recognized.

In Example 9, the subject matter of Example 8 optionally includes,wherein to adjust the sensor, the sensor fusion module is to: increase aresolution of a camera in the camera array.

In Example 10, the subject matter of any one or more of Examples 8-9optionally include, wherein to adjust the sensor, the sensor fusionmodule is to: increase a sampling rate of the sensor.

In Example 11, the subject matter of Example 10 optionally includes,wherein the sensor comprises one of: a camera, a radar sensor, a LIDARsensor, an ultrasonic sensor, or an infrared sensor.

In Example 12, the subject matter of any one or more of Examples 8-11optionally include, wherein to access the model in the route, the sensorfusion module is to: determine a location or a route of the vehicle;transmit the location or the route of the vehicle to a cloud service;and receive from the cloud service, the model of the road in the route.

In Example 13, the subject matter of any one or more of Examples 8-12optionally include, wherein the model is based on a plurality oftraversals over the road made by previous vehicles.

In Example 14, the subject matter of any one or more of Examples 8-13optionally include, wherein the model is revised using a machinelearning technique.

In Example 15, the subject matter of any one or more of Examples 1-14optionally include, further comprising an image capture module to:determine a gaze direction of a driver of the vehicle; and process animage from a camera of the camera array based on the gaze direction.

In Example 16, the subject matter of Example 15 optionally includes,wherein to process the image from the camera of the camera array basedon the gaze direction, the image capture module is to: capture theimage; and transmit the image to a cloud service.

In Example 17, the subject matter of any one or more of Examples 15-16optionally include, wherein to process the image from the camera of thecamera array based on the gaze direction, the image capture module isto: adjust the camera array based on the image to identify an object ofinterest in the image; and advise the driver based on the identificationof the object of interest.

Example 18 is a method of augmenting vehicle sensors, the methodcomprising: detecting at a vehicle control system in a vehicle, a speedof the vehicle; and adjusting by the vehicle control system, aforward-facing camera array based on the speed of the vehicle.

In Example 19, the subject matter of Example 18 optionally includes,wherein adjusting the camera array comprises: determining whether thespeed of the vehicle violates a threshold speed; controlling alow-resolution camera in the camera array to capture a low-resolutionimage; and controlling a high-resolution camera in the camera array tocapture an object identified in the low-resolution image.

In Example 20, the subject matter of Example 19 optionally includes,wherein controlling the high-resolution camera comprises: identifying anobject of interest in the low-resolution image using a simple objectclassifier; determining a location of the object of interest in thelow-resolution image; determining a portion of a high-resolution imagecaptured by the high-resolution camera that corresponds to the locationof the object of interest in the low-resolution image; and processingthe portion of the high-resolution image using a complex objectclassifier.

In Example 21, the subject matter of any one or more of Examples 19-20optionally include, wherein controlling the high-resolution cameracomprises: identifying an object of interest in the low-resolution imageusing a simple object classifier; determining a location of the objectof interest in the low-resolution image; controlling the high-resolutioncamera to frame the object of interest and capture a high-resolutionimage; and processing the high-resolution image using a complex objectclassifier.

In Example 22, the subject matter of Example 21 optionally includes,wherein controlling the high-resolution camera to frame the object ofinterest comprises: controlling at least one of a zoom or a focus toframe the object of interest.

In Example 23, the subject matter of any one or more of Examples 18-22optionally include, wherein adjusting the camera array comprises:adjusting one of a focal length or a resolution to identify objectsfarther away from the vehicle when the speed of the vehicle increases.

In Example 24, the subject matter of any one or more of Examples 18-23optionally include, wherein adjusting the camera array comprises:focusing a camera in the camera array on objects farther in front of thevehicle as the speed of the vehicle increases.

In Example 25, the subject matter of any one or more of Examples 18-24optionally include, further comprising: accessing a model of a road in aroute that the vehicle is travelling, the road having a featureindicating a dangerous portion of the road; using the camera array torecognize the feature; and adjusting a sensor in the vehicle when thefeature is recognized.

In Example 26, the subject matter of Example 25 optionally includes,wherein adjusting the sensor comprises: increasing a resolution of acamera in the camera array.

In Example 27, the subject matter of any one or more of Examples 25-26optionally include, wherein adjusting the sensor comprises: increasing asampling rate of the sensor.

In Example 28, the subject matter of Example 27 optionally includes,wherein the sensor comprises one of: a camera, a radar sensor, a LIDARsensor, an ultrasonic sensor, or an infrared sensor.

In Example 29, the subject matter of any one or more of Examples 25-28optionally include, wherein accessing the model in the route comprises:determining a location or a route of the vehicle; transmitting thelocation or the route of the vehicle to a cloud service; and receivingfrom the cloud service, the model of the road in the route.

In Example 30, the subject matter of any one or more of Examples 25-29optionally include, wherein the model is based on a plurality oftraversals over the road made by previous vehicles.

In Example 31, the subject matter of any one or more of Examples 25-30optionally include, wherein the model is revised using a machinelearning technique.

In Example 32, the subject matter of any one or more of Examples 18-31optionally include, further comprising: determining a gaze direction ofa driver of the vehicle; and processing an image from a camera of thecamera array based on the gaze direction.

In Example 33, the subject matter of Example 32 optionally includes,wherein processing the image from the camera of the camera array basedon the gaze direction comprises: capturing the image; and transmittingthe image to a cloud service.

In Example 34, the subject matter of any one or more of Examples 32-33optionally include, wherein processing the image from the camera of thecamera array based on the gaze direction comprises: adjusting the cameraarray based on the image to identify an object of interest in the image;and advising the driver based on the identification of the object ofinterest.

Example 35 is at least one machine-readable medium includinginstructions, which when executed by a machine, cause the machine toperform operations of any of the methods of Examples 18-34.

Example 36 is an apparatus comprising means for performing any of themethods of Examples 18-34.

Example 37 is an apparatus for augmenting vehicle sensors, the apparatuscomprising: means for detecting at a vehicle control system in avehicle, a speed of the vehicle; and means for adjusting by the vehiclecontrol system, a forward-facing camera array based on the speed of thevehicle.

In Example 38, the subject matter of Example 37 optionally includes,wherein adjusting the camera array comprises: determining whether thespeed of the vehicle violates a threshold speed; controlling alow-resolution camera in the camera array to capture a low-resolutionimage; and controlling a high-resolution camera in the camera array tocapture an object identified in the low-resolution image.

In Example 39, the subject matter of Example 38 optionally includes,wherein the means for controlling the high-resolution camera comprise:means for identifying an object of interest in the low-resolution imageusing a simple object classifier; means for determining a location ofthe object of interest in the low-resolution image; means fordetermining a portion of a high-resolution image captured by thehigh-resolution camera that corresponds to the location of the object ofinterest in the low-resolution image; and means for processing theportion of the high-resolution image using a complex object classifier.

In Example 40, the subject matter of any one or more of Examples 38-39optionally include, wherein the means for controlling thehigh-resolution camera comprise: means for identifying an object ofinterest in the low-resolution image using a simple object classifier;means for determining a location of the object of interest in thelow-resolution image; means for controlling the high-resolution camerato frame the object of interest and capture a high-resolution image; andmeans for processing the high-resolution image using a complex objectclassifier.

In Example 41, the subject matter of Example 40 optionally includes,wherein the means for controlling the high-resolution camera to framethe object of interest comprise: means for controlling at least one of azoom or a focus to frame the object of interest.

In Example 42, the subject matter of any one or more of Examples 37-41optionally include, wherein the means for adjusting the camera arraycomprise: means for adjusting one of a focal length or a resolution toidentify objects farther away from the vehicle when the speed of thevehicle increases.

In Example 43, the subject matter of any one or more of Examples 37-42optionally include, wherein the means for adjusting the camera arraycomprise: means for focusing a camera in the camera array on objectsfarther in front of the vehicle as the speed of the vehicle increases.

In Example 44, the subject matter of any one or more of Examples 37-43optionally include, further comprising: means for accessing a model of aroad in a route that the vehicle is travelling, the road having afeature indicating a dangerous portion of the road; means for using thecamera array to recognize the feature; and means for adjusting a sensorin the vehicle when the feature is recognized.

In Example 45, the subject matter of Example 44 optionally includes,wherein the means for adjusting the sensor comprise: means forincreasing a resolution of a camera in the camera array.

In Example 46, the subject matter of any one or more of Examples 44-45optionally include, wherein the means for adjusting the sensor comprise:means for increasing a sampling rate of the sensor.

In Example 47, the subject matter of Example 46 optionally includes,wherein the sensor comprises one of: a camera, a radar sensor, a LIDARsensor, an ultrasonic sensor, or an infrared sensor.

In Example 48, the subject matter of any one or more of Examples 44-47optionally include, wherein the means for accessing the model in theroute comprise: means for determining a location or a route of thevehicle; means for transmitting the location or the route of the vehicleto a cloud service; and means for receiving from the cloud service, themodel of the road in the route.

In Example 49, the subject matter of any one or more of Examples 44-48optionally include, wherein the model is based on a plurality oftraversals over the road made by previous vehicles.

In Example 50, the subject matter of any one or more of Examples 44-49optionally include, wherein the model is revised using a machinelearning technique.

In Example 51, the subject matter of any one or more of Examples 37-50optionally include, further comprising: means for determining a gazedirection of a driver of the vehicle; and means for processing an imagefrom a camera of the camera array based on the gaze direction.

In Example 52, the subject matter of Example 51 optionally includes,wherein the means for processing the image from the camera of the cameraarray based on the gaze direction comprise: means for capturing theimage; and means for transmitting the image to a cloud service.

In Example 53, the subject matter of any one or more of Examples 51-52optionally include, wherein the means for processing the image from thecamera of the camera array based on the gaze direction comprise: meansfor adjusting the camera array based on the image to identify an objectof interest in the image; and means for advising the driver based on theidentification of the object of interest.

Example 54 is a system for augmented vehicle sensors, the systemcomprising: a processor subsystem; and a memory including instructions,which when executed by the processor subsystem, cause the processorsubsystem to: detect a speed of a vehicle; and adjust a forward-facingcamera array based on the speed of the vehicle.

In Example 55, the subject matter of Example 54 optionally includes,wherein the instructions to adjust the camera array compriseinstructions to: determine whether the speed of the vehicle violates athreshold speed; control a low-resolution camera in the camera array tocapture a low-resolution image; and control a high-resolution camera inthe camera array to capture an object identified in the low-resolutionimage.

In Example 56, the subject matter of Example 55 optionally includes,wherein the instructions to control the high-resolution camera compriseinstructions to: identify an object of interest in the low-resolutionimage using a simple object classifier; determine a location of theobject of interest in the low-resolution image; determine a portion of ahigh-resolution image captured by the high-resolution camera thatcorresponds to the location of the object of interest in thelow-resolution image; and process the portion of the high-resolutionimage using a complex object classifier.

In Example 57, the subject matter of any one or more of Examples 55-56optionally include, wherein the instructions to control thehigh-resolution camera comprise instructions to: identify an object ofinterest in the low-resolution image using a simple object classifier;determine a location of the object of interest in the low-resolutionimage; control the high-resolution camera to frame the object ofinterest and capture a high-resolution image; and process thehigh-resolution image using a complex object classifier.

In Example 58, the subject matter of Example 57 optionally includes,wherein the instructions to control the high-resolution camera to framethe object of interest comprise instructions to: control at least one ofa zoom or a focus to frame the object of interest.

In Example 59, the subject matter of any one or more of Examples 54-58optionally include, wherein the instructions to adjust the camera arraycomprise instructions to: adjust one of a focal length or a resolutionto identify objects farther away from the vehicle when the speed of thevehicle increases.

In Example 60, the subject matter of any one or more of Examples 54-59optionally include, wherein the instructions to adjust the camera arraycomprise instructions to: focus a camera in the camera array on objectsfarther in front of the vehicle as the speed of the vehicle increases.

In Example 61, the subject matter of any one or more of Examples 54-60optionally include, further comprising instructions to: access a modelof a road in a route that the vehicle is travelling, the road having afeature indicating a dangerous portion of the road; use the camera arrayto recognize the feature; and adjust a sensor in the vehicle when thefeature is recognized.

In Example 62, the subject matter of Example 61 optionally includes,wherein to adjust the sensor comprise instructions to: increase aresolution of a camera in the camera array.

In Example 63, the subject matter of any one or more of Examples 61-62optionally include, wherein the instructions to adjust the sensorcomprise instructions to: increase a sampling rate of the sensor.

In Example 64, the subject matter of Example 63 optionally includes,wherein the sensor comprises one of: a camera, a radar sensor, a LIDARsensor, an ultrasonic sensor, or an infrared sensor.

In Example 65, the subject matter of any one or more of Examples 61-64optionally include, wherein the instructions to access the model in theroute comprise instructions to: determine a location or a route of thevehicle; transmit the location or the route of the vehicle to a cloudservice; and receive from the cloud service, the model of the road inthe route.

In Example 66, the subject matter of any one or more of Examples 61-65optionally include, wherein the model is based on a plurality oftraversals over the road made by previous vehicles.

In Example 67, the subject matter of any one or more of Examples 61-66optionally include, wherein the model is revised using a machinelearning technique.

In Example 68, the subject matter of any one or more of Examples 54-67optionally include, further comprising instructions to: determine a gazedirection of a driver of the vehicle; and process an image from a cameraof the camera array based on the gaze direction.

In Example 69, the subject matter of Example 68 optionally includes,wherein the instructions to process the image from the camera of thecamera array based on the gaze direction comprise instructions to:capture the image; and transmit the image to a cloud service.

In Example 70, the subject matter of any one or more of Examples 68-69optionally include, wherein the instructions to process the image fromthe camera of the camera array based on the gaze direction compriseinstructions to: adjust the camera array based on the image to identifyan object of interest in the image; and advise the driver based on theidentification of the object of interest.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments that may bepracticed. These embodiments are also referred to herein as “examples.”Such examples may include elements in addition to those shown ordescribed. However, also contemplated are examples that include theelements shown or described. Moreover, also contemplated are examplesusing any combination or permutation of those elements shown ordescribed (or one or more aspects thereof), either with respect to aparticular example (or one or more aspects thereof), or with respect toother examples (or one or more aspects thereof) shown or describedherein.

Publications, patents, and patent documents referred to in this documentare incorporated by reference herein in their entirety, as thoughindividually incorporated by reference. In the event of inconsistentusages between this document and those documents so incorporated byreference, the usage in the incorporated reference(s) are supplementaryto that of this document; for irreconcilable inconsistencies, the usagein this document controls.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended, that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim are still deemed to fall within thescope of that claim. Moreover, in the following claims, the terms“first,” “second,” and “third,” etc. are used merely as labels, and arenot intended to suggest a numerical order for their objects.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with others. Otherembodiments may be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is to allow thereader to quickly ascertain the nature of the technical disclosure. Itis submitted with the understanding that it will not be used tointerpret or limit the scope or meaning of the claims. Also, in theabove Detailed Description, various features may be grouped together tostreamline the disclosure. However, the claims may not set forth everyfeature disclosed herein as embodiments may feature a subset of saidfeatures. Further, embodiments may include fewer features than thosedisclosed in a particular example. Thus, the following claims are herebyincorporated into the Detailed Description, with a claim standing on itsown as a separate embodiment. The scope of the embodiments disclosedherein is to be determined with reference to the appended claims, alongwith the full scope of equivalents to which such claims are entitled.

What is claimed is:
 1. A system for managing an autonomous vehicle, thesystem comprising: a vehicle control system in a vehicle to: detect aspeed of the vehicle; and adjust a forward-facing camera array based onthe speed of the vehicle, wherein to adjust the camera array, thevehicle control system is to: determine whether the speed of the vehicleviolates a threshold speed: control a low-resolution camera in thecamera array to capture a low-resolution image; and control ahigh-resolution camera in the camera array to capture an objectidentified in the low-resolution image.
 2. The system of claim 1,wherein to control the high-resolution camera, the vehicle controlsystem is to: identify an object of interest in the low-resolution imageusing a simple object classifier, determine a location of the object ofinterest in the low-resolution image; determine a portion of ahigh-resolution image captured by the high-resolution camera thatcorresponds to the location of the object of interest in thelow-resolution image; and process the portion of the high-resolutionimage using a complex object classifier.
 3. The system of claim 1,wherein to control the high-resolution camera, the vehicle controlsystem is to: identify an object of interest in the low-resolution imageusing a simple object classifier, determine a location of the object ofinterest in the low-resolution image; control the high-resolution camerato frame the object of interest and capture a high-resolution image; andprocess the high-resolution image using a complex object classifier. 4.The system of claim 3, wherein to control the high-resolution camera toframe the object of interest, the vehicle control system is to: controlat least one of a zoom or a focus to frame the object of interest.
 5. Asystem for managing an autonomous vehicle, the system comprising: avehicle control system in a vehicle to: detect a speed of the vehicle;and adjust a forward-facing camera array based on the speed of thevehicle; and a sensor fusion module to: access a model of a road in aroute that the vehicle is travelling, the road having a featureindicating a dangerous portion of the road; use the camera array torecognize the feature; and adjust a sensor in the vehicle when thefeature is recognized.
 6. The system of claim 5, wherein to adjust thesensor, the sensor fusion module is to: increase a resolution of acamera in the camera array.
 7. The system of claim 5, wherein to adjustthe sensor, the sensor fusion module is to: increase a sampling rate ofthe sensor.
 8. The system of claim 7, wherein the sensor comprises oneof: a camera, a radar sensor, a LIDAR sensor, an ultrasonic sensor, oran infrared sensor.
 9. The system of claim 5, wherein to access themodel in the route, the sensor fusion module is to: determine a locationor a route of the vehicle; transmit the location or the route of thevehicle to a cloud service; and receive from the cloud service, themodel of the road in the route.
 10. The system of claim 5, wherein themodel is based on a plurality of traversals over the road made byprevious vehicles.
 11. The system of claim 5, wherein the model isrevised using a machine learning technique.
 12. A system for managing anautonomous vehicle, the system comprising: a vehicle control system in avehicle to: detect a speed of the vehicle; and adjust a forward-facingcamera array based on the speed of the vehicle; and an image capturemodule to: determine a gaze direction of a driver of the vehicle; andprocess an image from a camera of the camera array based on the gazedirection, wherein to process the image from the camera of the cameraarray based on the gaze direction, the image capture module is to:capture the image; and transmit the image to a cloud service.
 13. Thesystem of claim 12, wherein to process the image from the camera of thecamera array based on the gaze direction, the image capture module isto: adjust the camera array based on the image to identify an object ofinterest in the image; and advise the driver based on the identificationof the object of interest.
 14. A method of augmenting vehicle sensors,the method comprising: detecting at a vehicle control system in avehicle, a speed of the vehicle; and adjusting by the vehicle controlsystem, a forward-facing camera array based on the speed of the vehicle,wherein adjusting the camera array comprises: determining whether thespeed of the vehicle violates a threshold speed; controlling alow-resolution camera in the camera array to capture a low-resolutionimage; and controlling a high-resolution camera in the camera array tocapture an object identified in the low-resolution image.
 15. The methodof claim 14, wherein controlling the high-resolution camera comprises:identifying an object of interest in the low-resolution image using asimple object classifier, determining a location of the object ofinterest in the low-resolution image; determining a portion of ahigh-resolution image captured by the high-resolution camera thatcorresponds to the location of the object of interest in thelow-resolution image; and processing the portion of the high-resolutionimage using a complex object classifier.
 16. The method of claim 14,wherein controlling the high-resolution camera comprises: identifying anobject of interest in the low-resolution image using a simple objectclassifier, determining a location of the object of interest in thelow-resolution image; controlling the high-resolution camera to framethe object of interest and capture a high-resolution image; andprocessing the high-resolution image using a complex object classifier.17. The method of claim 16, wherein controlling the high-resolutioncamera to frame the object of interest comprises: controlling at leastone of a zoom or a focus to frame the object of interest.
 18. The methodof claim 14, wherein adjusting the camera array comprises: adjusting oneof a focal length or a resolution to identify objects farther away fromthe vehicle when the speed of the vehicle increases.
 19. At least onenon-transitory machine-readable medium including instructions, whichwhen executed by a machine, cause the machine to: detect at a vehiclecontrol system in a vehicle, a speed of the vehicle; and adjust by thevehicle control system, a forward-facing camera array based on the speedof the vehicle, wherein the instructions to adjust the camera arraycomprise instructions to: determine whether the speed of the vehicleviolates a threshold speed; control a low-resolution camera in thecamera array to capture a low-resolution image; and control ahigh-resolution camera in the camera array to capture an objectidentified in the low-resolution image.
 20. The at least onemachine-readable medium of claim 19, wherein the instructions to controlthe high-resolution camera comprise instructions to: identify an objectof interest in the low-resolution image using a simple objectclassifier; determine a location of the object of interest in thelow-resolution image; determine a portion of a high-resolution imagecaptured by the high-resolution camera that corresponds to the locationof the object of interest in the low-resolution image; and process theportion of the high-resolution image using a complex object classifier.